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Jonathan D. Cohen 0003
Jonathan Cohen 0003
Person information
- affiliation: Princeton University, Princeton Neuroscience Institute, NJ, USA
- affiliation: University of Pittsburgh, PA, USA
- affiliation (PhD 1990): Carnegie Mellon University, Pittsburgh, PA, USA
Other persons with the same name
- Jonathan D. Cohen 0001 — Lawrence Livermore National Laboratory, CA, USA (and 2 more)
- Jonathan D. Cohen 0002 — National Security Agency, Fort Meade, MD, USA
- Jonathan D. Cohen 0004 — Duquesne University, Department of Mathematics and Computer Science, Pittsburgh, PA, USA
- Jonathan Cohen — disambiguation page
- Jonathan Cohen 0001 — Normandy University, Caen, France
- Jonathan Cohen 0002
— University of Haifa, Israel
- Jonathan Cohen 0004 — Duke University Medical Center, Department of Head and Neck Surgery and Communication Sciences, Durham, NC, USA
- Jonathan Cohen 0005 — De Paul University, Department of Mathematics, Chicago, IL, USA
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2020 – today
- 2024
- [j26]Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner:
Erratum: Multitasking Capacity: Hardness Results and Improved Constructions. SIAM J. Discret. Math. 38(2): 2001-2003 (2024) - [c53]Awni Altabaa, Taylor Whittington Webb, Jonathan D. Cohen, John Lafferty:
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers. ICLR 2024 - [c52]Shanka Subhra Mondal, Jonathan D. Cohen, Taylor Whittington Webb:
Slot Abstractors: Toward Scalable Abstract Visual Reasoning. ICML 2024 - [i28]Declan Campbell, Sreejan Kumar, Tyler Giallanza, Thomas L. Griffiths, Jonathan D. Cohen:
Human-Like Geometric Abstraction in Large Pre-trained Neural Networks. CoRR abs/2402.04203 (2024) - [i27]Declan Campbell, Jonathan D. Cohen:
A Relational Inductive Bias for Dimensional Abstraction in Neural Networks. CoRR abs/2402.18426 (2024) - [i26]Shanka Subhra Mondal, Jonathan D. Cohen, Taylor W. Webb:
Slot Abstractors: Toward Scalable Abstract Visual Reasoning. CoRR abs/2403.03458 (2024) - 2023
- [j25]Sreejan Kumar
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. PLoS Comput. Biol. 19(8) (2023) - [c51]Javier Alejandro Masís, David Melnikoff, Lisa Feldman Barrett, Jonathan Cohen:
When to choose: Information seeking in the speed-accuracy tradeoff. CogSci 2023 - [c50]Aleksandra Mroczko-Wasowicz, Casey O'Callaghan, Jonathan Cohen, Brian J. Scholl, Philip J. Kellman:
Advances in the Study of Visual and Multisensory Objects. CogSci 2023 - [c49]Harrison Ritz, William Wolf, Jonathan Cohen:
Continuous and Discrete Transitions during Task-Switching. CogSci 2023 - [c48]Simon N. Segert, Jonathan Cohen:
Beyond Transformers for Function Learning. CogSci 2023 - [c47]Shanka Subhra Mondal, Taylor Whittington Webb, Jonathan Cohen:
Learning to reason over visual objects. ICLR 2023 - [c46]Taylor W. Webb, Shanka Subhra Mondal, Jonathan D. Cohen:
Systematic Visual Reasoning through Object-Centric Relational Abstraction. NeurIPS 2023 - [i25]Shanka Subhra Mondal, Taylor Whittington Webb, Jonathan D. Cohen:
Learning to reason over visual objects. CoRR abs/2303.02260 (2023) - [i24]Awni Altabaa, Taylor W. Webb, Jonathan D. Cohen, John Lafferty:
Abstractors: Transformer Modules for Symbolic Message Passing and Relational Reasoning. CoRR abs/2304.00195 (2023) - [i23]Simon N. Segert, Jonathan D. Cohen:
Beyond Transformers for Function Learning. CoRR abs/2304.09979 (2023) - [i22]Shanka Subhra Mondal, Steven Frankland, Taylor W. Webb, Jonathan D. Cohen:
Determinantal Point Process Attention Over Grid Codes Supports Out of Distribution Generalization. CoRR abs/2305.18417 (2023) - [i21]Taylor W. Webb, Shanka Subhra Mondal, Jonathan D. Cohen:
Systematic Visual Reasoning through Object-Centric Relational Abstraction. CoRR abs/2306.02500 (2023) - [i20]Ryan Pyle, Sebastian Musslick, Jonathan D. Cohen, Ankit B. Patel:
A Quantitative Approach to Predicting Representational Learning and Performance in Neural Networks. CoRR abs/2307.07575 (2023) - [i19]Raghavendra Pradyumna Pothukuchi, Leon Lufkin, Yu Jun Shen, Alejandro Simon, Rome Thorstenson, Bernardo Eilert Trevisan, Michael Tu, Mudi Yang, Ben Foxman, Viswanatha Srinivas Pothukuchi, Gunnar Epping, Bryant J. Jongkees, Thi Ha Kyaw, Jerome R. Busemeyer, Jonathan D. Cohen, Abhishek Bhattacharjee:
Quantum Cognitive Modeling: New Applications and Systems Research Directions. CoRR abs/2309.00597 (2023) - [i18]Taylor W. Webb, Steven M. Frankland, Awni Altabaa, Kamesh Krishnamurthy, Declan Campbell, Jacob L. Russin
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction. CoRR abs/2309.06629 (2023) - 2022
- [j24]Chaitanya K. Baru, Michael Pozmantier, Ilkay Altintas, Stephen Baek, Jonathan Cohen, Laura E. Condon, Giulia Fanti, Raul Castro Fernandez, Ethan Jackson, Upmanu Lall, Bennett A. Landman, Hai Li, Claudia Marin, Beatriz Martínez-López, Dimitris N. Metaxas, Bradley D. Olsen, Grier P. Page
Enabling AI Innovation via Data and Model Sharing: An Overview of the Nsf Convergence Accelerator Track D. AI Mag. 43(1): 93-104 (2022) - [j23]Marius Catalin Iordan, Tyler Giallanza, Cameron T. Ellis
Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora. Cogn. Sci. 46(2) (2022) - [j22]Grant Wallace, Stephen Polcyn, Paula P. Brooks, Anne C. Mennen, Ke Zhao, Paul S. Scotti, Sebastian Michelmann, Kai Li, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman:
RT-Cloud: A cloud-based software framework to simplify and standardize real-time fMRI. NeuroImage 257: 119295 (2022) - [j21]Simon N. Segert, Jonathan D. Cohen:
A Self-Supervised Framework for Function Learning and Extrapolation. Trans. Mach. Learn. Res. 2022 (2022) - [c45]Ján Veselý, Raghavendra Pradyumna Pothukuchi
Distill: Domain-Specific Compilation for Cognitive Models. CGO 2022: 301-312 - [c44]Simon N. Segert, Jonathan Cohen:
Maximum Entropy Function Learning. CogSci 2022 - [c43]Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins, Jonathan D. Cohen, Nathaniel D. Daw, Karthik Narasimhan, Tom Griffiths:
Using natural language and program abstractions to instill human inductive biases in machines. NeurIPS 2022 - [i17]Sreejan Kumar, Ishita Dasgupta, Raja Marjieh, Nathaniel D. Daw
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine Learning. CoRR abs/2204.01437 (2022) - [i16]Zack Dulberg, Rachit Dubey, Isabel M. Berwian, Jonathan D. Cohen:
Modularity benefits reinforcement learning agents with competing homeostatic drives. CoRR abs/2204.06608 (2022) - [i15]Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh, Michael Y. Hu, Robert D. Hawkins
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines. CoRR abs/2205.11558 (2022) - 2021
- [c42]Zack Dulberg, Taylor W. Webb, Jonathan Cohen:
Modelling the development of counting with memory-augmented neural networks. CogSci 2021 - [c41]Gregory Henselman-Petrusek, Tyler Giallanza, Sebastian Musslick, Jonathan Cohen:
Regression, encoding, control: an integrated approach to shared representations with distributed coding. CogSci 2021 - [c40]Sreejan Kumar, Ishita Dasgupta, Jonathan D. Cohen, Nathaniel D. Daw, Thomas L. Griffiths:
Meta-Learning of Structured Task Distributions in Humans and Machines. ICLR 2021 - [c39]Taylor Whittington Webb, Ishan Sinha, Jonathan D. Cohen:
Emergent Symbols through Binding in External Memory. ICLR 2021 - [i14]Mark K. Ho, David Abel, Carlos G. Correa, Michael L. Littman, Jonathan D. Cohen, Thomas L. Griffiths:
Control of mental representations in human planning. CoRR abs/2105.06948 (2021) - [i13]Zack Dulberg, Taylor W. Webb, Jonathan Cohen:
Modelling the development of counting with memory-augmented neural networks. CoRR abs/2105.10577 (2021) - [i12]Simon N. Segert, Jonathan D. Cohen:
A Self-Supervised Framework for Function Learning and Extrapolation. CoRR abs/2106.07369 (2021) - [i11]Ján Veselý, Raghavendra Pradyumna Pothukuchi, Ketaki Joshi, Samyak Gupta, Jonathan D. Cohen, Abhishek Bhattacharjee:
Cognac: Domain-Specific Compilation for Cognitive Models. CoRR abs/2110.15425 (2021) - 2020
- [j20]Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths
Multitasking Capacity: Hardness Results and Improved Constructions. SIAM J. Discret. Math. 34(1): 885-903 (2020) - [c38]Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths:
People Do Not Just Plan, They Plan to Plan. AAAI 2020: 1300-1307 - [c37]Steven Frankland, Jonathan Cohen:
Determinantal Point Processes for Memory and Structured Inference. CogSci 2020 - [c36]Sebastian Musslick, Maria Wirzberger, Ivan Grahek, Laura Bustamante, Amitai Shenhav, Jonathan D. Cohen:
Mental effort: One construct, many faces? CogSci 2020 - [c35]Lena Rosendahl, Anastasia S. Bizyaeva, Jonathan Cohen:
A Novel Quantum Approach to the Dynamics of Decision Making. CogSci 2020 - [c34]Ishan Sinha, Jonathan Cohen, Taylor W. Webb:
A memory-augmented neural network model of abstract sequential reasoning. CogSci 2020 - [c33]Taylor W. Webb, Zachary Dulberg, Steven Frankland, Alexander A. Petrov, Randall C. O'Reilly, Jonathan Cohen:
Learning Representations that Support Extrapolation. ICML 2020: 10136-10146 - [i10]Mark K. Ho, David Abel, Jonathan D. Cohen, Michael L. Littman, Thomas L. Griffiths:
The Efficiency of Human Cognition Reflects Planned Information Processing. CoRR abs/2002.05769 (2020) - [i9]Taylor W. Webb, Zachary Dulberg, Steven M. Frankland, Alexander A. Petrov, Randall C. O'Reilly, Jonathan D. Cohen:
Learning Representations that Support Extrapolation. CoRR abs/2007.05059 (2020) - [i8]Sachin Ravi, Sebastian Musslick, Maia Hamin, Theodore L. Willke, Jonathan D. Cohen:
Navigating the Trade-Off between Multi-Task Learning and Learning to Multitask in Deep Neural Networks. CoRR abs/2007.10527 (2020) - [i7]Sreejan Kumar
Meta-Learning of Compositional Task Distributions in Humans and Machines. CoRR abs/2010.02317 (2020) - [i6]Zachary Dulberg, Jonathan D. Cohen:
Learning Canonical Transformations. CoRR abs/2011.08822 (2020) - [i5]Ishan Sinha, Taylor W. Webb, Jonathan D. Cohen:
A Memory-Augmented Neural Network Model of Abstract Rule Learning. CoRR abs/2012.07172 (2020) - [i4]Taylor W. Webb, Ishan Sinha, Jonathan D. Cohen:
Emergent Symbols through Binding in External Memory. CoRR abs/2012.14601 (2020)
2010 – 2019
- 2019
- [c32]Sebastian Musslick, Abigail Novick Hoskin, Taylor W. Webb, Steven Frankland, Jonathan D. Cohen, Rebecca L. Jackson, Matthew A. Lambon Ralph, Lang Chen, Timothy T. Rogers, Randall C. O'Reilly, Alexander A. Petrov:
Understanding interactions amongst cognitive control, learning and representation. CogSci 2019: 35-36 - [c31]Sebastian Musslick, Jonathan D. Cohen:
A Mechanistic Account of Constraints on Control-Dependent Processing: Shared Representation, Conflict and Persistence. CogSci 2019: 849-855 - [c30]Markus Spitzer, Sebastian Musslick, Michael Shvartsman, Amitai Shenhav, Jonathan D. Cohen:
Asymmetric Switch Costs as a Function of Task Strength. CogSci 2019: 1070-1076 - [c29]Steven Frankland, Taylor W. Webb, Alexander A. Petrov, Randall C. O'Reilly, Jonathan Cohen:
Extracting and Utilizing Abstract, Structured Representations for Analogy. CogSci 2019: 1766-1772 - [c28]Sebastian Musslick, Anastasia S. Bizyaeva, Shamay Agaron, Naomi Ehrich Leonard, Jonathan D. Cohen:
Stability-Flexibility Dilemma in Cognitive Control: A Dynamical System Perspective. CogSci 2019: 2420-2426 - [c27]Sebastian Musslick, Jonathan D. Cohen, Amitai Shenhav:
Decomposing Individual Differences in Cognitive Control: A Model-Based Approach. CogSci 2019: 2427-2433 - [c26]Taylor W. Webb, Steven Frankland, Simon N. Segert, Alexander A. Petrov, Randall C. O'Reilly, Jonathan Cohen:
A tradeoff between generalization and perceptual capacity in recurrent neural networks. CogSci 2019: 3603 - [i3]Marius Catalin Iordan, Tyler Giallanza, Cameron T. Ellis, Nicole Beckage, Jonathan D. Cohen:
Context Matters: Recovering Human Semantic Structure from Machine Learning Analysis of Large-Scale Text Corpora. CoRR abs/1910.06954 (2019) - 2018
- [c25]Michael Shvartsman, Narayanan Sundaram, Mikio Aoi, Adam Charles, Theodore L. Willke, Jonathan D. Cohen:
Matrix-normal models for fMRI analysis. AISTATS 2018: 1914-1923 - [c24]Laura Bustamante, Augustus Baker, Allison Burton, Amitai Shenhav, Chloe Hoeber, Nathaniel D. Daw, Jonathan Cohen:
Novel methods for measuring the cost of cognitive control in a patch foraging task and a demand selection task with Stroop. CogSci 2018 - [c23]Marius Catalin Iordan, Cameron T. Ellis, Michael Lesnick, Daniel N. Osherson, Jonathan Cohen:
Feature Ratings and Empirical Dimension-Specific Similarity Explain Distinct Aspects of Semantic Similarity Judgments. CogSci 2018 - [c22]Sebastian Musslick, Jonathan D. Cohen, Amitai Shenhav:
Estimating the costs of cognitive control from task performance: theoretical validation and potential pitfalls. CogSci 2018 - [c21]Sebastian Musslick, Seong Jun Jang, Michael Shvartsman, Amitai Shenhav, Jonathan D. Cohen:
Constraints associated with cognitive control and the stability-flexibility dilemma. CogSci 2018 - [c20]Yotam Sagiv, Sebastian Musslick, Yael Niv, Jonathan D. Cohen:
Efficiency of learning vs. processing: Towards a normative theory of multitasking. CogSci 2018 - [i2]Noga Alon, Jonathan D. Cohen, Thomas L. Griffiths, Pasin Manurangsi, Daniel Reichman, Igor Shinkar, Tal Wagner, Alexander Y. Ku:
Multitasking Capacity: Hardness Results and Improved Constructions. CoRR abs/1809.02835 (2018) - 2017
- [j19]Vikranth R. Bejjanki, Rava Azeredo da Silveira
Noise correlations in the human brain and their impact on pattern classification. PLoS Comput. Biol. 13(8) (2017) - [c19]Gary Kane, Aaron M. Bornstein, Robert Wilson, Amitai Shenhav, Nathaniel D. Daw, Jonathan Cohen:
Mechanisms of overharvesting in patch foraging. CogSci 2017 - [c18]Olga Lositsky, Michael Shvartsman, Robert C. Wilson, Jonathan D. Cohen:
Adaptive response priors in context-dependent decision-making. CogSci 2017 - [c17]Sebastian Musslick, Andrew Saxe, Kayhan Özcimder, Biswadip Dey, Greg Henselman, Jonathan D. Cohen:
Multitasking Capability Versus Learning Efficiency in Neural Network Architectures. CogSci 2017 - [c16]Kayhan Özcimder, Biswadip Dey, Sebastian Musslick, Giovanni Petri, Nesreen K. Ahmed, Theodore L. Willke, Jonathan D. Cohen:
A Formal Approach to Modeling the Cost of Cognitive Control. CogSci 2017 - [c15]Noga Alon, Daniel Reichman, Igor Shinkar, Tal Wagner, Sebastian Musslick, Jonathan D. Cohen, Tom Griffiths, Biswadip Dey, Kayhan Özcimder:
A graph-theoretic approach to multitasking. NIPS 2017: 2100-2109 - 2016
- [c14]Yida Wang, Bryn Keller
Real-time full correlation matrix analysis of fMRI data. IEEE BigData 2016: 1242-1251 - [c13]Andra Geana, Robert Wilson, Nathaniel D. Daw, Jonathan Cohen:
Boredom, Information-Seeking and Exploration. CogSci 2016 - [c12]Andra Geana, Robert Wilson, Nathaniel D. Daw, Jonathan Cohen:
Information-Seeking, Learning and the Marginal Value Theorem: A Normative Approach to Adaptive Exploration. CogSci 2016 - [c11]Sebastian Musslick, Biswadip Dey, Kayhan Özcimder, Md. Mostofa Ali Patwary, Theodore L. Willke, Jonathan D. Cohen:
Controlled vs. Automatic Processing: A Graph-Theoretic Approach to the Analysis of Serial vs. Parallel Processing in Neural Network Architectures. CogSci 2016 - [i1]Jonathan D. Cohen, Biswadip Dey, Tom Griffiths, Sebastian Musslick, Kayhan Özcimder, Daniel Reichman, Igor Shinkar, Tal Wagner:
A Graph-Theoretic Approach to Multitasking. CoRR abs/1611.02400 (2016) - 2015
- [c10]Michael Shvartsman, Vaibhav Srivastava, Jonathan D. Cohen:
A Theory of Decision Making Under Dynamic Context. NIPS 2015: 2485-2493 - [c9]Yida Wang, Michael J. Anderson, Jonathan D. Cohen, Alexander Heinecke, Kai Li, Nadathur Satish, Narayanan Sundaram, Nicholas B. Turk-Browne, Theodore L. Willke:
Full correlation matrix analysis of fMRI data on Intel® Xeon Phi™ coprocessors. SC 2015: 23:1-23:12 - 2014
- [j18]Matthew M. Botvinick, Jonathan D. Cohen:
The Computational and Neural Basis of Cognitive Control: Charted Territory and New Frontiers. Cogn. Sci. 38(6): 1249-1285 (2014) - [j17]Philip Holmes, Jonathan D. Cohen:
Optimality and Some of Its Discontents: Successes and Shortcomings of Existing Models for Binary Decisions. Top. Cogn. Sci. 6(2): 258-278 (2014) - [c8]Jeremy R. Manning
Hierarchical topographic factor analysis. PRNI 2014: 1-4 - 2013
- [j16]Michael T. Todd, Leigh E. Nystrom, Jonathan D. Cohen:
Confounds in multivariate pattern analysis: Theory and rule representation case study. NeuroImage 77: 157-165 (2013) - 2012
- [j15]Andrea Nedic, Damon Tomlin, Philip Holmes, Deborah A. Prentice, Jonathan D. Cohen:
A Decision Task in a Social Context: Human Experiments, Models, and Analyses of Behavioral Data. Proc. IEEE 100(3): 713-733 (2012) - 2011
- [j14]Sarah E. Forster, Cameron S. Carter, Jonathan D. Cohen, Raymond Y. Cho:
Parametric Manipulation of the Conflict Signal and Control-state Adaptation. J. Cogn. Neurosci. 23(4): 923-935 (2011) - [j13]Nick Yeung
Errors of interpretation and modeling: A reply to Grinband et al. NeuroImage 57(2): 316-319 (2011) - [c7]Marieke K. van Vugt, Patrick Simen, Jonathan Cohen:
Finding neural correlates of drift diffusion processes in EEG oscillations. CogSci 2011
2000 – 2009
- 2009
- [j12]Juan Gao, KongFatt Wong-Lin
Sequential Effects in Two-Choice Reaction Time Tasks: Decomposition and Synthesis of Mechanisms. Neural Comput. 21(9): 2407-2436 (2009) - 2008
- [j11]Yuan Sophie Liu, Philip Holmes, Jonathan D. Cohen:
A Neural Network Model of the Eriksen Task: Reduction, Analysis, and Data Fitting. Neural Comput. 20(2): 345-373 (2008) - [j10]Eric Shea-Brown, Mark S. Gilzenrat, Jonathan D. Cohen:
Optimization of Decision Making in Multilayer Networks: The Role of Locus Coeruleus. Neural Comput. 20(12): 2863-2894 (2008) - [c6]Andrea Nedic, Damon Tomlin, Philip Holmes, Deborah A. Prentice, Jonathan D. Cohen:
A simple decision task in a social context: Experiments, a model, and preliminary analyses of behavioral data. CDC 2008: 1115-1120 - [c5]Michael T. Todd, Yael Niv, Jonathan D. Cohen:
Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement. NIPS 2008: 1689-1696 - [c4]Angela J. Yu, Jonathan D. Cohen:
Sequential effects: Superstition or rational behavior? NIPS 2008: 1873-1880 - 2006
- [j9]Patrick Simen, Jonathan D. Cohen, Philip Holmes:
Rapid decision threshold modulation by reward rate in a neural network. Neural Networks 19(8): 1013-1026 (2006) - 2005
- [j8]Philip Holmes, Eric Shea-Brown, Jeff Moehlis, Rafal Bogacz, Juan Gao, Gary Aston-Jones, Ed Clayton, Janusz Rajkowski, Jonathan D. Cohen:
Optimal Decisions: From Neural Spikes, through Stochastic Differential Equations, to Behavior. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 88-A(10): 2496-2503 (2005) - [j7]Eric Brown, Juan Gao, Philip Holmes, Rafal Bogacz, Mark S. Gilzenrat, Jonathan D. Cohen:
Simple Neural Networks that Optimize Decisions. Int. J. Bifurc. Chaos 15(3): 803-826 (2005) - [c3]Samuel McClure, Mark S. Gilzenrat, Jonathan D. Cohen:
An exploration-exploitation model based on norepinepherine and dopamine activity. NIPS 2005: 867-874 - 2004
- [j6]James K. Rilling, Alan G. Sanfey
The neural correlates of theory of mind within interpersonal interactions. NeuroImage 22(4): 1694-1703 (2004) - 2003
- [j5]Kate Fissell, Eugene Tseytlin, Daniel Cunningha, Karthickeyan Iyer, Cameron S. Carter, Walter Schneider, Jonathan D. Cohen:
Fiswidgets - A graphical computing environment for neuroimaging analysis. Neuroinformatics 1(1): 111-125 (2003) - 2002
- [j4]P. Read Montague, Gregory S. Berns, Jonathan D. Cohen, Samuel M. McClure, Giuseppe Pagnoni
Hyperscanning: Simultaneous fMRI during Linked Social Interactions. NeuroImage 16(4): 1159-1164 (2002) - [j3]Mark S. Gilzenrat, Benjamin D. Holmes, Janusz Rajkowski, Gary Aston-Jones, Jonathan D. Cohen:
Simplified dynamics in a model of noradrenergic modulation of cognitive performance. Neural Networks 15(4-6): 647-663 (2002) - 2001
- [j2]Vincent van Veen, Jonathan D. Cohen, Matthew M. Botvinick, V. Andrew Stenger, Cameron S. Carter:
Anterior Cingulate Cortex, Conflict Monitoring, and Levels of Processing. NeuroImage 14(6): 1302-1308 (2001)
1990 – 1999
- 1997
- [j1]Nigel H. Goddard, Greg Hood, Jonathan Cohen, William F. Eddy, Christopher R. Genovese, Douglas C. Noll, Leigh E. Nystrom:
Online Analysis of Functional MRI Datasets on Parallel Platforms. J. Supercomput. 11(3): 295-318 (1997) - 1994
- [c2]Todd S. Braver, Jonathan D. Cohen, David Servan-Schreiber:
A Computational Model of Prefrontal Cortex Function. NIPS 1994: 141-148
1980 – 1989
- 1989
- [c1]David Servan-Schreiber, Harry Printz, Jonathan D. Cohen:
The Effect of Catecholamines on Performance: From Unit to System Behavior. NIPS 1989: 100-108
Coauthor Index
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